Molmo2 delivers state-of-the-art open-weight video VLMs with new grounding datasets and training methods that outperform prior open models and match or exceed some proprietary ones on pointing and tracking tasks.
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MOT20: A benchmark for multi object tracking in crowded scenes
16 Pith papers cite this work. Polarity classification is still indexing.
16
Pith papers citing it
abstract
Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. The benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal to establish a standardized evaluation of multiple object tracking methods. The challenge focuses on multiple people tracking, since pedestrians are well studied in the tracking community, and precise tracking and detection has high practical relevance. Since the first release, MOT15, MOT16, and MOT17 have tremendously contributed to the community by introducing a clean dataset and precise framework to benchmark multi-object trackers. In this paper, we present our MOT20benchmark, consisting of 8 new sequences depicting very crowded challenging scenes. The benchmark was presented first at the 4thBMTT MOT Challenge Workshop at the Computer Vision and Pattern Recognition Conference (CVPR) 2019, and gives to chance to evaluate state-of-the-art methods for multiple object tracking when handling extremely crowded scenarios.
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representative citing papers
A pose estimation-based tracking framework with specialized modules for underwater salmon scenes outperforms standard trackers on new datasets and supports tail beat analysis for welfare indicators.
A closed-set wasp-tracking benchmark shows current MOT trackers lose identities over thousands of frames even with perfect detections.
Polycepta recursively estimates per-object appearance states for tracking-by-detection MOT, improving identity preservation over time and reporting 92.27% MOTA on KITTI at 90.57 Hz.
Presents a new egocentric HRI dataset and reports that an optimized tracking pipeline with ReID reduces identity switches by 49% over baseline.
GateMOT proposes Q-Gated Attention to enable linear-complexity, spatially aware attention for state-of-the-art dense object tracking on benchmarks like BEE24.
SatSAM2 improves satellite video object tracking by integrating Kalman motion priors and a motion state machine into SAM2, outperforming prior methods with a 5.84% AUC gain on OOTB while introducing the MVOT synthetic benchmark.
EASE-MCVT is a distributed edge-assisted multi-camera vehicle tracking framework that achieves real-time performance and competitive accuracy on public datasets through edge processing and server-side optimizations.
OmniTrack++ improves omnidirectional multi-object tracking with trajectory feedback through DynamicSSM stabilization, FlexiTrack instances, ExpertTrack Memory with Mixture-of-Experts, and adaptive Tracklet Management, achieving SOTA HOTA gains on JRDB and new EmboTrack benchmark.
PS-Track sets a new state-of-the-art for point-supervised multi-object tracking by converting point seeds into temporally consistent pseudo-labels via Temporal-Feedback Prompting, Point-Excited Wavelet Attention, and Uncertainty-Guided Gaussian Learning.
SAMOFT improves multi-object tracking by using SAM segmentation and optical flow for pixel-level motion matching, flexible centroid correction, and training-free motion pattern fixes on top of standard Kalman and ReID baselines.
TCMP achieves SOTA MOT metrics (HOTA 63.4%, IDF1 65.0%, AssA 49.1%) with 0.014x parameters and 0.05x FLOPs of the previous best method by using a simple dilated TCN regressor.
HyperSSM integrates hypergraphs and state space models to let correlated objects mutually refine motion estimates, stabilizing trajectories under noise and occlusion for state-of-the-art multi-object tracking.
SVAG-Bench is a new large-scale benchmark for multi-instance spatio-temporal video action grounding with dense annotations from urban, wildlife, and surveillance videos.
NOOUGAT unifies online and offline multi-object tracking with a GNN that processes non-overlapping subclips fused by an Autoregressive Long-term Tracking layer, reporting SOTA gains on DanceTrack, SportsMOT, and MOT20.
A survey that taxonomizes non-Transformer vision models and evaluates their practical trade-offs across efficiency, scalability, and robustness.
citing papers explorer
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Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding
Molmo2 delivers state-of-the-art open-weight video VLMs with new grounding datasets and training methods that outperform prior open models and match or exceed some proprietary ones on pointing and tracking tasks.
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A Multi-purpose Tracking Framework for Salmon Welfare Monitoring in Challenging Environments
A pose estimation-based tracking framework with specialized modules for underwater salmon scenes outperforms standard trackers on new datasets and supports tail beat analysis for welfare indicators.
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WaspMOT: A Benchmark for Long-Term Multi-Object Tracking of Trichogramma Wasps
A closed-set wasp-tracking benchmark shows current MOT trackers lose identities over thousands of frames even with perfect detections.
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Polycepta: Object-Centric Appearance Estimation for Multi-Object Tracking
Polycepta recursively estimates per-object appearance states for tracking-by-detection MOT, improving identity preservation over time and reporting 92.27% MOTA on KITTI at 90.57 Hz.
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Face versus Body Tracking for Human-Robot Interaction: An Egocentric Dataset
Presents a new egocentric HRI dataset and reports that an optimized tracking pipeline with ReID reduces identity switches by 49% over baseline.
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GateMOT: Q-Gated Attention for Dense Object Tracking
GateMOT proposes Q-Gated Attention to enable linear-complexity, spatially aware attention for state-of-the-art dense object tracking on benchmarks like BEE24.
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SatSAM2: Motion-Constrained Video Object Tracking in Satellite Imagery using Promptable SAM2 and Kalman Priors
SatSAM2 improves satellite video object tracking by integrating Kalman motion priors and a motion state machine into SAM2, outperforming prior methods with a 5.84% AUC gain on OOTB while introducing the MVOT synthetic benchmark.
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Edge Assisted Multi-Camera Vehicle Tracking Framework for Real-Time and Scalable Deployment
EASE-MCVT is a distributed edge-assisted multi-camera vehicle tracking framework that achieves real-time performance and competitive accuracy on public datasets through edge processing and server-side optimizations.
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OmniTrack++: Omnidirectional Multi-Object Tracking by Learning Large-FoV Trajectory Feedback
OmniTrack++ improves omnidirectional multi-object tracking with trajectory feedback through DynamicSSM stabilization, FlexiTrack instances, ExpertTrack Memory with Mixture-of-Experts, and adaptive Tracklet Management, achieving SOTA HOTA gains on JRDB and new EmboTrack benchmark.
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PS-MOT: Cultivating Instance Awareness from Point Seeds for Multi-Object Tracking
PS-Track sets a new state-of-the-art for point-supervised multi-object tracking by converting point seeds into temporally consistent pseudo-labels via Temporal-Feedback Prompting, Point-Excited Wavelet Attention, and Uncertainty-Guided Gaussian Learning.
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SAMOFT: Robust Multi-Object Tracking via Region and Flow
SAMOFT improves multi-object tracking by using SAM segmentation and optical flow for pixel-level motion matching, flexible centroid correction, and training-free motion pattern fixes on top of standard Kalman and ReID baselines.
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Time-series Meets Complex Motion Modeling: Robust and Computational-effective Motion Predictor for Multi-object Tracking
TCMP achieves SOTA MOT metrics (HOTA 63.4%, IDF1 65.0%, AssA 49.1%) with 0.014x parameters and 0.05x FLOPs of the previous best method by using a simple dilated TCN regressor.
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Hypergraph-State Collaborative Reasoning for Multi-Object Tracking
HyperSSM integrates hypergraphs and state space models to let correlated objects mutually refine motion estimates, stabilizing trajectories under noise and occlusion for state-of-the-art multi-object tracking.
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SVAG-Bench: A Large-Scale Benchmark for Multi-Instance Spatio-temporal Video Action Grounding
SVAG-Bench is a new large-scale benchmark for multi-instance spatio-temporal video action grounding with dense annotations from urban, wildlife, and surveillance videos.
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NOOUGAT: Towards Unified Online and Offline Multi-Object Tracking
NOOUGAT unifies online and offline multi-object tracking with a GNN that processes non-overlapping subclips fused by an Autoregressive Long-term Tracking layer, reporting SOTA gains on DanceTrack, SportsMOT, and MOT20.
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Attention Is not Everything: Efficient Alternatives for Vision
A survey that taxonomizes non-Transformer vision models and evaluates their practical trade-offs across efficiency, scalability, and robustness.